ABSTRACT The study of non-parametric methods Classification Tree Analysis (CTA) using data mining techniques for remote sensing applications is still much to do, so it require studies on the CTA's ability to handle quite a lot of data, by utilizing CTA advantages for remote sensing applications. The combination of parameter CTA and data input, as well as its application on two different detail levels of land use classification scheme, require testing related to the level of accuracy that is generated. This studies aimed to simulate multiple combinations of parameters to determine the level of accuracy of the classification results, and obtain a decision tree from KDD results. And to analyze the accuracy of non-parametric methods CTA with data mining techniques for land use classification using Landsat-8 OLI, and apply the results of KDD on another area. The classification is obtained by performing simulations on several parameters CTA and data input. There are three splitting rules parameter in CTA, i.e. Ratio, Entropy, and Gini. Pruning parameter, i.e. 0%, 1%, 5%, and 10%. There are several inputs of the classification data, namely seven bands of Landsat-8 OLI imagery, image transformation (NDVI, NDWI, BI, NDBI, and PCA), as well as texture filter variance and texture filter mean (moving window 3x3 and 5x5). Non-spectral data, i.e.elevation data and slope data. And two-level land use classification scheme, i.e. Level I (5 classes) and Level II (8 classes). The decision tree that obtained from the best accuracy of the learning outcomes then applied to another area with similar characteristics. The results showed that the best parameter combination are a splitting attribute Gini, pruning 1%, texture filter with 5x5 moving window,and Level I scheme, with an accuracy of 96.71%, kappa 0.9504, and processing time 3.388 sec. Its aplication on another area, resulting an overall accuracy 93.27% with kappa 0.8923.The best accuracy rate obtained in this study and its application on another area was greater than 90%. Therefore this method is expected could be an alternative method for land use policy application, and land use classification based on land cover commensurate to a scale of 1:100,000.